Conventional database systems store large volumes of data. The data typically conforms to a logical schema which characterizes the data and exposes relationships within the data. Analytics applications leverage this logical schema to provide visualizations, such as charts and graphs, which present selected data in an intuitive format.
More specifically, conventional systems map logical entities of a database's schema to a set of abstract entities known as business objects. The business objects may represent business entities, such as customers, time periods, financial figures (e.g., sales, profit) etc. Business objects may be classified as dimensions (along which one may want to perform an analysis), and measures (e.g., indicators, most often numeric, whose value can be determined for a given combination of dimension values).
A user selects business objects in order to create a visualization presenting data associated with the business objects. For example, a user may select a Country dimension and a Sales measure. A visualization is then generated which shows total sales for each country represented in the database. In many instances, however, it can be difficult for a user to determine suitable measures and/or dimensions to select for inclusion in a visualization.